论文标题
剧本质量评估：我们可以预测谁被提名吗？
Screenplay Quality Assessment: Can We Predict Who Gets Nominated?
论文作者
论文摘要
决定哪些脚本变成电影是电影制片人的昂贵且耗时的过程。因此，建立一个工具来帮助脚本选择，即电影制作的初始阶段，可能非常有益。为了实现这一目标，在这项工作中，我们提出了一种根据语言提示评估剧本质量的方法。我们以两倍的方法解决了这一点：（1）我们将任务定义为在主要电影奖中预测脚本的提名，假设同伴认可的脚本应该有更大的成功机会。 （2）基于行业的意见和叙事学，我们将特定于领域的特征提取和集成到共同的分类技术中。我们面临两个挑战（1）脚本比其他文档数据集（2）提名脚本的时间更长，因此很难收集。但是，借助叙事学启发的建模和领域功能，我们的方法对强基础有明显的改进。我们的工作为剧本分析中的未来工作提供了一种新的方法。
Deciding which scripts to turn into movies is a costly and time-consuming process for filmmakers. Thus, building a tool to aid script selection, an initial phase in movie production, can be very beneficial. Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues. We address this in a two-fold approach: (1) we define the task as predicting nominations of scripts at major film awards with the hypothesis that the peer-recognized scripts should have a greater chance to succeed. (2) based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques. We face two challenges (1) scripts are much longer than other document datasets (2) nominated scripts are limited and thus difficult to collect. However, with narratology-inspired modeling and domain features, our approach offers clear improvements over strong baselines. Our work provides a new approach for future work in screenplay analysis.
